Abstract / truncated to 115 words (read the full abstract)

Adaptive filtering algorithms are powerful signal processing tools with widespread use in numerous engineering applications. Computational complexity is a key factor in determining the optimal implementation as well as real-time performance of the adaptive signal processors. To minimize the required hardware and/or software resources for implementing an adaptive filtering algorithm, it is desirable to mitigate its computational complexity as much as possible without imposing any significant sacrifice of performance. This thesis comprises a collection of thirteen peer-reviewed published works as well as an integrating material. The works are along the lines of a common unifying theme that is to devise new low-complexity adaptive filtering algorithms for communications and, more generally, signal processing applications. The main ... toggle 6 keywords

adaptive filtering computational complexity linearly-constrained adaptive estimation performance analysis set-membership filtering system identification

Information

Author
Arablouei, Reza
Institution
University of South Australia
Supervisor
Publication Year
2013
Upload Date
April 9, 2014

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